Show Me Your Work Decision Log
cursor/plugins
Keeps a TSV decision log for long or unattended agent runs, one row per decision with what, why, evidence and result, so a reviewer can check the work later.
Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.
$ npx skills add affaan-m/ECC --skill autonomous-loops -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install affaan-m/ECC autonomous-loops --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/autonomous-loops .claude/skills/autonomous-loops && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "autonomous-loops" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/autonomous-loops into .claude/skills/autonomous-loops/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "autonomous-loops", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/affaan-m/ECC/tree/main/skills/autonomous-loopsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add affaan-m/ECC --skill autonomous-loops -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install affaan-m/ECC autonomous-loops --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/autonomous-loops .agents/skills/autonomous-loops && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "autonomous-loops" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/autonomous-loops into .agents/skills/autonomous-loops/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "autonomous-loops", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add affaan-m/ECC --skill autonomous-loops -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install affaan-m/ECC autonomous-loops --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/autonomous-loops .cursor/skills/autonomous-loops && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "autonomous-loops" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/autonomous-loops into .cursor/skills/autonomous-loops/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "autonomous-loops", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/affaan-m/ECC.git --path skills/autonomous-loops--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add affaan-m/ECC --skill autonomous-loops -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install affaan-m/ECC autonomous-loops --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/autonomous-loops .gemini/skills/autonomous-loops && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "autonomous-loops" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/autonomous-loops into .gemini/skills/autonomous-loops/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "autonomous-loops", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install affaan-m/ECC autonomous-loopsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add affaan-m/ECC --skill autonomous-loops -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/autonomous-loops .github/skills/autonomous-loops && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "autonomous-loops" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/autonomous-loops into .github/skills/autonomous-loops/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "autonomous-loops", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add affaan-m/ECC --skill autonomous-loops -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install affaan-m/ECC autonomous-loops --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/autonomous-loops .opencode/skills/autonomous-loops && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "autonomous-loops" agent skill from https://github.com/affaan-m/ECC/tree/main/skills/autonomous-loops into .opencode/skills/autonomous-loops/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "autonomous-loops", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
autonomous-loopsPatterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.
Autonomous Loops is an agent skill from affaan-m/ECC. Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems. Retained for compatibility only: when new autonomous loop guidance is needed, use continuous-agent-loop instead.
Its SKILL.md is about 5.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Agent Workflows, covering Autonomous loops. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 2d515e4. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
claudenodeghnpmFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use gh and npm, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Autonomous Loops loads about 5.8k tokens when it runs. Until then it costs about 66 tokens; SKILL.md has 1,665 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from affaan-m/ECC at commit 2d515e4, republished under its MIT licence (© affaan-m). 1,665 words, ~5,809 tokens.
.claude/skills/autonomous-loops/SKILL.md (or your agent's skills folder).Compatibility note (v1.8.0):
autonomous-loopsis retained for one release. The canonical skill name is nowcontinuous-agent-loop. New loop guidance should be authored there, while this skill remains available to avoid breaking existing workflows.
Patterns, architectures, and reference implementations for running Claude Code autonomously in loops. Covers everything from simple claude -p pipelines to full RFC-driven multi-agent DAG orchestration.
From simplest to most sophisticated:
| Pattern | Complexity | Best For |
|---|---|---|
| Sequential Pipeline | Low | Daily dev steps, scripted workflows |
| NanoClaw REPL | Low | Interactive persistent sessions |
| Infinite Agentic Loop | Medium | Parallel content generation, spec-driven work |
| Continuous Claude PR Loop | Medium | Multi-day iterative projects with CI gates |
| De-Sloppify Pattern | Add-on | Quality cleanup after any Implementer step |
| Ralphinho / RFC-Driven DAG | High | Large features, multi-unit parallel work with merge queue |
claude -p)The simplest loop. Break daily development into a sequence of non-interactive claude -p calls. Each call is a focused step with a clear prompt.
If you can't figure out a loop like this, it means you can't even drive the LLM to fix your code in interactive mode.
The claude -p flag runs Claude Code non-interactively with a prompt, exits when done. Chain calls to build a pipeline:
#!/bin/bash
# daily-dev.sh — Sequential pipeline for a feature branch
set -e
# Step 1: Implement the feature
claude -p "Read the spec in docs/auth-spec.md. Implement OAuth2 login in src/auth/. Write tests first (TDD). Do NOT create any new documentation files."
# Step 2: De-sloppify (cleanup pass)
claude -p "Review all files changed by the previous commit. Remove any unnecessary type tests, overly defensive checks, or testing of language features (e.g., testing that TypeScript generics work). Keep real business logic tests. Run the test suite after cleanup."
# Step 3: Verify
claude -p "Run the full build, lint, type check, and test suite. Fix any failures. Do not add new features."
# Step 4: Commit
claude -p "Create a conventional commit for all staged changes. Use 'feat: add OAuth2 login flow' as the message."claude -p call means no context bleed between steps.set -e stops the pipeline on failure.With model routing:
# Research with Opus (deep reasoning)
claude -p --model opus "Analyze the codebase architecture and write a plan for adding caching..."
# Implement with Sonnet (fast, capable)
claude -p "Implement the caching layer according to the plan in docs/caching-plan.md..."
# Review with Opus (thorough)
claude -p --model opus "Review all changes for security issues, race conditions, and edge cases..."With environment context:
# Pass context via files, not prompt length
echo "Focus areas: auth module, API rate limiting" > .claude-context.md
claude -p "Read .claude-context.md for priorities. Work through them in order."
rm .claude-context.mdWith --allowedTools restrictions:
# Read-only analysis pass
claude -p --allowedTools "Read,Grep,Glob" "Audit this codebase for security vulnerabilities..."
# Write-only implementation pass
claude -p --allowedTools "Read,Write,Edit,Bash" "Implement the fixes from security-audit.md..."ECC's built-in persistent loop. A session-aware REPL that calls claude -p synchronously with full conversation history.
# Start the default session
node scripts/claw.js
# Named session with skill context
CLAW_SESSION=my-project CLAW_SKILLS=tdd-workflow,security-review node scripts/claw.js~/.claude/claw/{session}.mdclaude -p with full history as context| Use Case | NanoClaw | Sequential Pipeline |
|---|---|---|
| Interactive exploration | Yes | No |
| Scripted automation | No | Yes |
| Session persistence | Built-in | Manual |
| Context accumulation | Grows per turn | Fresh each step |
| CI/CD integration | Poor | Excellent |
See the /claw command documentation for full details.
A two-prompt system that orchestrates parallel sub-agents for specification-driven generation. Developed by disler (credit: @disler).
PROMPT 1 (Orchestrator) PROMPT 2 (Sub-Agents)
┌─────────────────────┐ ┌──────────────────────┐
│ Parse spec file │ │ Receive full context │
│ Scan output dir │ deploys │ Read assigned number │
│ Plan iteration │────────────│ Follow spec exactly │
│ Assign creative dirs │ N agents │ Generate unique output │
│ Manage waves │ │ Save to output dir │
└─────────────────────┘ └──────────────────────┘Create .claude/commands/infinite.md:
Parse the following arguments from $ARGUMENTS:
1. spec_file — path to the specification markdown
2. output_dir — where iterations are saved
3. count — integer 1-N or "infinite"
PHASE 1: Read and deeply understand the specification.
PHASE 2: List output_dir, find highest iteration number. Start at N+1.
PHASE 3: Plan creative directions — each agent gets a DIFFERENT theme/approach.
PHASE 4: Deploy sub-agents in parallel (Task tool). Each receives:
- Full spec text
- Current directory snapshot
- Their assigned iteration number
- Their unique creative direction
PHASE 5 (infinite mode): Loop in waves of 3-5 until context is low.Invoke:
/project:infinite specs/component-spec.md src/ 5
/project:infinite specs/component-spec.md src/ infinite| Count | Strategy |
|---|---|
| 1-5 | All agents simultaneously |
| 6-20 | Batches of 5 |
| infinite | Waves of 3-5, progressive sophistication |
Don't rely on agents to self-differentiate. The orchestrator assigns each agent a specific creative direction and iteration number. This prevents duplicate concepts across parallel agents.
A production-grade shell script that runs Claude Code in a continuous loop, creating PRs, waiting for CI, and merging automatically. Created by AnandChowdhary (credit: @AnandChowdhary).
┌─────────────────────────────────────────────────────┐
│ CONTINUOUS CLAUDE ITERATION │
│ │
│ 1. Create branch (continuous-claude/iteration-N) │
│ 2. Run claude -p with enhanced prompt │
│ 3. (Optional) Reviewer pass — separate claude -p │
│ 4. Commit changes (claude generates message) │
│ 5. Push + create PR (gh pr create) │
│ 6. Wait for CI checks (poll gh pr checks) │
│ 7. CI failure? → Auto-fix pass (claude -p) │
│ 8. Merge PR (squash/merge/rebase) │
│ 9. Return to main → repeat │
│ │
│ Limit by: --max-runs N | --max-cost $X │
│ --max-duration 2h | completion signal │
└─────────────────────────────────────────────────────┘Warning: Install continuous-claude from its repository after reviewing the code. Do not pipe external scripts directly to bash.
# Basic: 10 iterations
continuous-claude --prompt "Add unit tests for all untested functions" --max-runs 10
# Cost-limited
continuous-claude --prompt "Fix all linter errors" --max-cost 5.00
# Time-boxed
continuous-claude --prompt "Improve test coverage" --max-duration 8h
# With code review pass
continuous-claude \
--prompt "Add authentication feature" \
--max-runs 10 \
--review-prompt "Run npm test && npm run lint, fix any failures"
# Parallel via worktrees
continuous-claude --prompt "Add tests" --max-runs 5 --worktree tests-worker &
continuous-claude --prompt "Refactor code" --max-runs 5 --worktree refactor-worker &
waitThe critical innovation: a SHARED_TASK_NOTES.md file persists across iterations:
## Progress
- [x] Added tests for auth module (iteration 1)
- [x] Fixed edge case in token refresh (iteration 2)
- [ ] Still need: rate limiting tests, error boundary tests
## Next Steps
- Focus on rate limiting module next
- The mock setup in tests/helpers.ts can be reusedClaude reads this file at iteration start and updates it at iteration end. This bridges the context gap between independent claude -p invocations.
When PR checks fail, Continuous Claude automatically:
gh run listclaude -p with CI fix contextgh run view, fixes code, commits, pushes--ci-retry-max attempts)Claude can signal "I'm done" by outputting a magic phrase:
continuous-claude \
--prompt "Fix all bugs in the issue tracker" \
--completion-signal "CONTINUOUS_CLAUDE_PROJECT_COMPLETE" \
--completion-threshold 3 # Stops after 3 consecutive signalsThree consecutive iterations signaling completion stops the loop, preventing wasted runs on finished work.
| Flag | Purpose |
|---|---|
--max-runs N | Stop after N successful iterations |
--max-cost $X | Stop after spending $X |
--max-duration 2h | Stop after time elapsed |
--merge-strategy squash | squash, merge, or rebase |
--worktree <name> | Parallel execution via git worktrees |
--disable-commits | Dry-run mode (no git operations) |
--review-prompt "..." | Add reviewer pass per iteration |
--ci-retry-max N | Auto-fix CI failures (default: 1) |
An add-on pattern for any loop. Add a dedicated cleanup/refactor step after each Implementer step.
When you ask an LLM to implement with TDD, it takes "write tests" too literally:
typeof x === 'string')Adding "don't test type systems" or "don't add unnecessary checks" to the Implementer prompt has downstream effects:
Instead of constraining the Implementer, let it be thorough. Then add a focused cleanup agent:
# Step 1: Implement (let it be thorough)
claude -p "Implement the feature with full TDD. Be thorough with tests."
# Step 2: De-sloppify (separate context, focused cleanup)
claude -p "Review all changes in the working tree. Remove:
- Tests that verify language/framework behavior rather than business logic
- Redundant type checks that the type system already enforces
- Over-defensive error handling for impossible states
- Console.log statements
- Commented-out code
Keep all business logic tests. Run the test suite after cleanup to ensure nothing breaks."for feature in "${features[@]}"; do
# Implement
claude -p "Implement $feature with TDD."
# De-sloppify
claude -p "Cleanup pass: review changes, remove test/code slop, run tests."
# Verify
claude -p "Run build + lint + tests. Fix any failures."
# Commit
claude -p "Commit with message: feat: add $feature"
doneRather than adding negative instructions which have downstream quality effects, add a separate de-sloppify pass. Two focused agents outperform one constrained agent.
The most sophisticated pattern. An RFC-driven, multi-agent pipeline that decomposes a spec into a dependency DAG, runs each unit through a tiered quality pipeline, and lands them via an agent-driven merge queue. Created by enitrat (credit: @enitrat).
RFC/PRD Document
│
▼
DECOMPOSITION (AI)
Break RFC into work units with dependency DAG
│
▼
┌──────────────────────────────────────────────────────┐
│ RALPH LOOP (up to 3 passes) │
│ │
│ For each DAG layer (sequential, by dependency): │
│ │
│ ┌── Quality Pipelines (parallel per unit) ───────┐ │
│ │ Each unit in its own worktree: │ │
│ │ Research → Plan → Implement → Test → Review │ │
│ │ (depth varies by complexity tier) │ │
│ └────────────────────────────────────────────────┘ │
│ │
│ ┌── Merge Queue ─────────────────────────────────┐ │
│ │ Rebase onto main → Run tests → Land or evict │ │
│ │ Evicted units re-enter with conflict context │ │
│ └────────────────────────────────────────────────┘ │
│ │
└──────────────────────────────────────────────────────┘AI reads the RFC and produces work units:
interface WorkUnit {
id: string; // kebab-case identifier
name: string; // Human-readable name
rfcSections: string[]; // Which RFC sections this addresses
description: string; // Detailed description
deps: string[]; // Dependencies (other unit IDs)
acceptance: string[]; // Concrete acceptance criteria
tier: "trivial" | "small" | "medium" | "large";
}Decomposition Rules:
The dependency DAG determines execution order:
Layer 0: [unit-a, unit-b] ← no deps, run in parallel
Layer 1: [unit-c] ← depends on unit-a
Layer 2: [unit-d, unit-e] ← depend on unit-cDifferent tiers get different pipeline depths:
| Tier | Pipeline Stages |
|---|---|
| trivial | implement → test |
| small | implement → test → code-review |
| medium | research → plan → implement → test → PRD-review + code-review → review-fix |
| large | research → plan → implement → test → PRD-review + code-review → review-fix → final-review |
This prevents expensive operations on simple changes while ensuring architectural changes get thorough scrutiny.
Each stage runs in its own agent process with its own context window:
| Stage | Model | Purpose |
|---|---|---|
| Research | Sonnet | Read codebase + RFC, produce context doc |
| Plan | Opus | Design implementation steps |
| Implement | Codex | Write code following the plan |
| Test | Sonnet | Run build + test suite |
| PRD Review | Sonnet | Spec compliance check |
| Code Review | Opus | Quality + security check |
| Review Fix | Codex | Address review issues |
| Final Review | Opus | Quality gate (large tier only) |
Critical design: The reviewer never wrote the code it reviews. This eliminates author bias — the most common source of missed issues in self-review.
After quality pipelines complete, units enter the merge queue:
Unit branch
│
├─ Rebase onto main
│ └─ Conflict? → EVICT (capture conflict context)
│
├─ Run build + tests
│ └─ Fail? → EVICT (capture test output)
│
└─ Pass → Fast-forward main, push, delete branchFile Overlap Intelligence:
Eviction Recovery: When evicted, full context is captured (conflicting files, diffs, test output) and fed back to the implementer on the next Ralph pass:
## MERGE CONFLICT — RESOLVE BEFORE NEXT LANDING
Your previous implementation conflicted with another unit that landed first.
Restructure your changes to avoid the conflicting files/lines below.
{full eviction context with diffs}research.contextFilePath ──────────────────→ plan
plan.implementationSteps ──────────────────→ implement
implement.{filesCreated, whatWasDone} ─────→ test, reviews
test.failingSummary ───────────────────────→ reviews, implement (next pass)
reviews.{feedback, issues} ────────────────→ review-fix → implement (next pass)
final-review.reasoning ────────────────────→ implement (next pass)
evictionContext ───────────────────────────→ implement (after merge conflict)Every unit runs in an isolated worktree (uses jj/Jujutsu, not git):
/tmp/workflow-wt-{unit-id}/Pipeline stages for the same unit share a worktree, preserving state (context files, plan files, code changes) across research → plan → implement → test → review.
| Signal | Use Ralphinho | Use Simpler Pattern |
|---|---|---|
| Multiple interdependent work units | Yes | No |
| Need parallel implementation | Yes | No |
| Merge conflicts likely | Yes | No (sequential is fine) |
| Single-file change | No | Yes (sequential pipeline) |
| Multi-day project | Yes | Maybe (continuous-claude) |
| Spec/RFC already written | Yes | Maybe |
| Quick iteration on one thing | No | Yes (NanoClaw or pipeline) |
Is the task a single focused change?
├─ Yes → Sequential Pipeline or NanoClaw
└─ No → Is there a written spec/RFC?
├─ Yes → Do you need parallel implementation?
│ ├─ Yes → Ralphinho (DAG orchestration)
│ └─ No → Continuous Claude (iterative PR loop)
└─ No → Do you need many variations of the same thing?
├─ Yes → Infinite Agentic Loop (spec-driven generation)
└─ No → Sequential Pipeline with de-sloppifyThese patterns compose well:
Sequential Pipeline + De-Sloppify — The most common combination. Every implement step gets a cleanup pass.
Continuous Claude + De-Sloppify — Add --review-prompt with a de-sloppify directive to each iteration.
Any loop + Verification — Use ECC's /verify command or verification-loop skill as a gate before commits.
Ralphinho's tiered approach in simpler loops — Even in a sequential pipeline, you can route simple tasks to Haiku and complex tasks to Opus:
# Simple formatting fix
claude -p --model haiku "Fix the import ordering in src/utils.ts"
# Complex architectural change
claude -p --model opus "Refactor the auth module to use the strategy pattern"Infinite loops without exit conditions — Always have a max-runs, max-cost, max-duration, or completion signal.
No context bridge between iterations — Each claude -p call starts fresh. Use SHARED_TASK_NOTES.md or filesystem state to bridge context.
Retrying the same failure — If an iteration fails, don't just retry. Capture the error context and feed it to the next attempt.
Negative instructions instead of cleanup passes — Don't say "don't do X." Add a separate pass that removes X.
All agents in one context window — For complex workflows, separate concerns into different agent processes. The reviewer should never be the author.
Ignoring file overlap in parallel work — If two parallel agents might edit the same file, you need a merge strategy (sequential landing, rebase, or conflict resolution).
| Project | Author | Link |
|---|---|---|
| Ralphinho | enitrat | credit: @enitrat |
| Infinite Agentic Loop | disler | credit: @disler |
| Continuous Claude | AnandChowdhary | credit: @AnandChowdhary |
| NanoClaw | ECC | /claw command in this repo |
| Verification Loop | ECC | skills/verification-loop/ in this repo |
© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/autonomous-loops of affaan-m/ECC.
Open the folder on GitHubat commit 2d515e4
We found 10 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 4 other GitHub owners. This page covers the copy in affaan-m/ECC, which our catalogue first saw on October 7, 2026.
Autonomous Loops next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Autonomous Loops this skillaffaan-m/ECC | 277k | 4 repos | ~5.8k | Automated safety check: Pass | MIT | |
| Show Me Your Work Decision Logcursor/plugins | 11k | 8 repos | ~1.6k | Automated safety check: Pass | None | |
| Autoresearch Iteration Loopuditgoenka/autoresearch | 6.5k | 1 repos | ~2k | Automated safety check: Pass | MIT | |
| Install Loop Engineeringcobusgreyling/loop-engineering | 11k | 1 repos | ~648 | Automated safety check: Pass | MIT | |
| LoopyForward-Future/loopy | 3.2k | — | ~3.9k | Automated safety check: Pass | MIT | |
| AI Performance Improvement Plantanweai/pua | 20k | 2 repos | ~6.9k | Automated safety check: Pass | MIT |
cursor/plugins
Keeps a TSV decision log for long or unattended agent runs, one row per decision with what, why, evidence and result, so a reviewer can check the work later.
uditgoenka/autoresearch
Runs an autonomous modify, verify, keep-or-discard loop against any metric, with subcommands for planning, debugging, fixing, security audits, shipping and more.
cobusgreyling/loop-engineering
Installs Loop Engineering into a project through the single @cobusgreyling/loop CLI, scaffolding a report-only loop and a readiness score.
Forward-Future/loopy
Discover, find, compare, audit, repair, adapt, craft, run, debrief, save, and prepare repeatable AI-agent loops for publication.
tanweai/pua
Pushes an agent to exhaust every option, investigate before asking and take initiative beyond the literal request, instead of giving up or waiting passively.
loopx-project/loopx
Diagnoses surprising LoopX behavior, such as stale recommendations or tiny progress, assigns it to the responsible layer and repairs it at the lowest durable level.
affaan-m/ECC
Audits your installed Claude skills and commands for quality, with a quick mode for recently changed skills and a full mode that evaluates all of them through subagents.
affaan-m/ECC
Ingests, indexes, searches, edits and monitors video, audio and live streams through the VideoDB Python SDK, returning stream links, clips and timestamps.
affaan-m/ECC
Route broad documentation-governance requests to existing ECC skills and run an opt-in, read-only audit of mapped documentation roles, links, ADR indexes, and evidence references.
affaan-m/ECC
Scans installed skills for principles that recur across them and proposes rule-file changes: append, revise, add a section, create a file or leave as covered.
affaan-m/ECC
Builds DRAFT counterparty agreements from one markdown template and a small JSON spec per party, with clauses picked by the party's role.
affaan-m/ECC
Set an ECC-specific frontend design direction for production UI work.
Categories
Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems. Autonomous Loops is an agent skill from affaan-m/ECC. Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.
Autonomous Loops fits situations like: tasks that involve Autonomous loops.
Run `npx skills add affaan-m/ECC --skill autonomous-loops -a claude-code`. Or copy the skill folder (skills/autonomous-loops in affaan-m/ECC) into .claude/skills/autonomous-loops in your project. Claude Code loads it when a task matches its description.
Run `npx skills add affaan-m/ECC --skill autonomous-loops -a codex`. Or copy the skill folder (skills/autonomous-loops in affaan-m/ECC) into .agents/skills/autonomous-loops in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill autonomous-loops -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/autonomous-loops, .gemini/skills/autonomous-loops, .github/skills/autonomous-loops and .opencode/skills/autonomous-loops in your project.
Going by SKILL.md and its folder, Autonomous Loops needs the command-line tools its instructions call (claude, node, gh and npm).
SKILL.md contains no URLs. Its commands use gh and npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Autonomous Loops is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 5.8k tokens (SKILL.md is roughly 23k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Autonomous Loops: Show Me Your Work Decision Log (cursor/plugins, 11k stars), Autoresearch Iteration Loop (uditgoenka/autoresearch, 6.5k stars), Install Loop Engineering (cobusgreyling/loop-engineering, 11k stars) and Loopy (Forward-Future/loopy, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 276,673 GitHub stars. The repository holds 683 skills in this directory. The repository was last updated on October 11, 2026.
Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.